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Record W2972214746 · doi:10.4000/geomorphologie.13125

Évolution des terres salées dans le nord de l’estuaire du Saloum (Sénégal)

2019· article· fr· W2972214746 on OpenAlexaff
Bineta Faye, Dome Tine, Ndiaye Dethié, Diop Cheikh, Guilgane Faye, Aminata Ndiaye

Bibliographic record

VenueGéomorphologie relief processus environnement · 2019
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsGeographyForestryPhysical geographyGeology

Abstract

fetched live from OpenAlex

Le milieu d’étude, au Sénégal, est caractérisé par une forte salinisation des terres. Il est compris entre la lagune de Joal-Fadiouth et la limite de la rive droite de la ria du Saloum entre les latitudes 13°97’ et 14°32’ Nord et les longitudes 16°39’ et 16°20’ Ouest. Ces terres salées existent depuis le Quaternaire récent, mais leur évolution actuelle est amplifiée par la variabilité climatique au cours de ces dernières décennies et l’action de l’homme. L’objectif de cette recherche est d’analyser la dynamique des terres salées dans le nord de l’estuaire du Saloum. La démarche méthodologique adoptée est basée sur le traitement des images satellitaires Landsat MSS de 1973, TM de 1984, 1992, ETM+ de 2006 et OLI et TIRS de 2014. La classification supervisée par maximum de vraisemblance validée par des vérifications de terrain, par détermination des matrices de confusion et du calcul des indices de Kappa, a permis de caractériser la dynamique des terres salées. Les résultats obtenus par la classification ont montré une forte avancée des terres salées durant ces deux dernières décennies. De 1994 à 2014, les terres salées ont augmenté de 21 000 ha. Les terres salées gagnent chaque année 1 000 ha en moyenne depuis 1994.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.210
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2019
Admission routes1
Has abstractyes

Explore more

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